Triple

T10450739
Position Surface form Disambiguated ID Type / Status
Subject Berliner Bezirk Spandau E246414 entity
Predicate hasPart P35 FINISHED
Object Siemensstadt E361710 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Siemensstadt | Statement: [Berliner Bezirk Spandau, hasPart, Siemensstadt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Siemensstadt
Context triple: [Berliner Bezirk Spandau, hasPart, Siemensstadt]
  • A. Siemensstadt chosen
    Siemensstadt is a Berlin neighborhood historically shaped by the Siemens industrial works and noted for its early 20th-century modernist housing developments.
  • B. Maxvorstadt
    Maxvorstadt is a central Munich district known for its concentration of major art museums, universities, and cultural institutions.
  • C. Oststadt
    Oststadt is a central district of Hanover, Germany, known for its urban residential areas, cultural venues, and proximity to the city’s main commercial and administrative centers.
  • D. Lippendorf
    Lippendorf is a village in Saxony, Germany, historically notable as the birthplace of Katharina von Bora, the wife of Martin Luther.
  • E. Quadratestadt
    Quadratestadt is the German nickname for the city of Mannheim, referring to its distinctive grid-like layout of city blocks.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d381c04fe08190957c26c526a3b05a completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4fe0a6a548190a54212912f618e4e completed April 7, 2026, 12:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69d87efedf6c8190aa4b7bbe5f160eeb completed April 10, 2026, 4:39 a.m.
Created at: April 6, 2026, 12:17 p.m.